mes analyses pour le BEC cholera 2023
Dr Stéphane Yetna
2024-01-16
Chargement des library
importation de la base de données
enlever les espaces sur les entêtes, filtrer les années à juste celle 2023, nettoyer dans la colonne region le fait que:(Centre= CENTRE, Sud= sud)
llcholeraclean <- llcholera110124 %>%
mutate(
annee = substring(epiweek_date_notification_2, 5, 8)
) %>%
filter(annee == "2023") %>%
mutate(region_34 = ifelse(region_34=="CENTRE", "Centre", ifelse(region_34=="sud", "Sud", region_34)))creer un tableau avec les variables qui m’interessent
llcholeraclean <- llcholeraclean %>%
select(c("n_id_1", "epiweek_date_notification_2", "health_district_of_origin", "age_year_6", "sex_7", "hospitalisation_oui_non", "region_34", "health_district_notifying_13", "state_of_dehydratation_1_mild_2_moderate_3_severe_16", "site_of_case_management", "result_of_culture", "outcome_2_healed_3_dead_32", "hospitalisation_oui_non"))ajouter les colonnes Cas et Décès et cas confirmés par cultures (avec chacun des references spécifiques)
llcholeraclean <- llcholeraclean %>%
mutate(deces = ifelse(outcome_2_healed_3_dead_32 != 3, "vivant", "mort")) %>%
mutate(deces = ifelse(is.na(outcome_2_healed_3_dead_32), "vivant", deces))histogramme en facte-grid par région du niveau de sérvérité(la colonne state_of_dehydratation) en focntion des semaines épidémiologique(epiweek_date-notification_2) pour comparaison
llcholeraclean$state_of_dehydratation_1_mild_2_moderate_3_severe_16[ is.na(llcholeraclean$state_of_dehydratation_1_mild_2_moderate_3_severe_16)] <- 2
ggplot(llcholeraclean, aes(y = epiweek_date_notification_2, fill = factor(state_of_dehydratation_1_mild_2_moderate_3_severe_16))) +
geom_bar(stat = "count", position = "stack") +
facet_grid(rows = vars(region_34)) +
scale_fill_manual(values = c("1" = "green", "2" = "yellow", "3" = "red"),
name = "Niveau de sévérité",
labels = c("1" = "Légère", "2" = "Modérée", "3" = "Sévère")) +
labs(title = "Variation de la sévérité par région en fonction des semaines épidémiologiques",
x = "Niveau",
y = "Semaine épidémiologique") +
theme_minimal() +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Nous observons sur le graphique en facet-grid prensentant le niveau de
sévérité que l’épidémie a débuté de manière continue dès la première
semaine jusqu’à la 52e semaine de l’année, tant au Centre qu’au
littoral. Cependant, une caractéristique distincte émerge : la courbe
révèle un taux de sévérité significatif au stade élevé entre la semaine
15 et la semaine 24 dans ces deux régions. En examinant les autres
régions, nous constatons que dans le Sud, les cas se font ressentir
entre la semaine 16 et la semaine 27. Parallèlement, le Sud-Ouest
affiche une hausse des cas notable de la semaine 32 à la semaine 45. Il
est intéressant de noter que cette augmentation peut suggérer une
possible importation de cas de la région du littoral vers le Sud-Ouest,
probablement due à leur proximité géographique. Cette analyse temporelle
met en lumière des variations régionales dans la dynamique de
l’épidémie, soulignant la nécessité d’une approche différenciée pour les
interventions et la surveillance épidémiologique.
sévétité pour la region du Centre
ggplot(subset(llcholeraclean, region_34 == "Centre"),
aes(y = epiweek_date_notification_2, fill = factor(state_of_dehydratation_1_mild_2_moderate_3_severe_16))) +
geom_bar(stat = "count", position = "stack") +
scale_fill_manual(values = c("1" = "green", "2" = "yellow", "3" = "red"),
name = "Niveau de sévérité",
labels = c("1" = "Légère", "2" = "Modérée", "3" = "Sévère")) +
labs(title = "Variation de la sévérité pour la région du Centre en fonction des semaines épidémiologiques",
x = "Niveau",
y = "Semaine épidémiologique") +
theme_minimal() +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "bottom", # Positionner la légende en bas
legend.direction = "horizontal") +
facet_wrap(~region_34, scales = "free_y", ncol = 1)
Pendant les semaines 13 à 37, on observe une nette flambée des cas dans
la région du Centre, avec un pic de sévérité entre la semaine 16 et 26.
Cette augmentation significative conduit à un nombre croissant de
patients se présentant en clinique, principalement à un stade modéré de
l’épidémie. La dynamique du graphe met en lumière l’urgence de mesures
préventives et de gestion pour faire face à cette hausse soudaine de la
gravité des cas.
sévérité pour le Littoral
ggplot(subset(llcholeraclean, region_34 == "Littoral"),
aes(y = epiweek_date_notification_2, fill = factor(state_of_dehydratation_1_mild_2_moderate_3_severe_16))) +
geom_bar(stat = "count", position = "stack") +
scale_fill_manual(values = c("1" = "green", "2" = "yellow", "3" = "red"),
name = "Niveau de sévérité",
labels = c("1" = "Légère", "2" = "Modérée", "3" = "Sévère")) +
labs(title = "Variation de la sévérité pour la région du Littoral en fonction des semaines épidémiologiques",
x = "Niveau",
y = "Semaine épidémiologique") +
theme_minimal() +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "bottom", # Positionner la légende en bas
legend.direction = "horizontal") +
facet_wrap(~region_34, scales = "free_y", ncol = 1)
Au cours des semaines 1 à 27 dans la région du littoral, une évolution
régulière des cas a été constatée. Cependant, à partir de la semaine 28
jusqu’à la 44, une flambée considérable a émergé, marquée par une
augmentation significative des cas. Cette tendance abrupte souligne
l’importance d’une réponse rapide et ciblée pour atténuer l’impact
croissant de la situation dans la région.
Sévérité pour l’Ouest
ggplot(subset(llcholeraclean, region_34 == "Ouest"),
aes(y = epiweek_date_notification_2, fill = factor(state_of_dehydratation_1_mild_2_moderate_3_severe_16))) +
geom_bar(stat = "count", position = "stack") +
scale_fill_manual(values = c("1" = "green", "2" = "yellow", "3" = "red"),
name = "Niveau de sévérité",
labels = c("1" = "Légère", "2" = "Modérée", "3" = "Sévère")) +
labs(title = "Variation de la sévérité pour la région du Ouest en fonction des semaines épidémiologiques",
x = "Niveau",
y = "Semaine épidémiologique") +
theme_minimal() +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "bottom", # Positionner la légende en bas
legend.direction = "horizontal") +
facet_wrap(~region_34, scales = "free_y", ncol = 1)
Dans la région de l’Ouest, l’évolution des cas de choléra entre la
semaine 4 et la semaine 33 est remarquable. Notamment, des pics de taux
sévères sont observés aux semaines 7, 13, 14, et 30, mettant en évidence
des moments critiques au cours de cette période. Le reste de la courbe
indique principalement des cas modérés, soulignant la nécessité d’une
vigilance particulière pour contrôler la gravité de la maladie dans la
région.
Sévérité pour le Sud-Ouest
ggplot(subset(llcholeraclean, region_34 == "Sud-Ouest"),
aes(y = epiweek_date_notification_2, fill = factor(state_of_dehydratation_1_mild_2_moderate_3_severe_16))) +
geom_bar(stat = "count", position = "stack") +
scale_fill_manual(values = c("1" = "green", "2" = "yellow", "3" = "red"),
name = "Niveau de sévérité",
labels = c("1" = "Légère", "2" = "Modérée", "3" = "Sévère")) +
labs(title = "Variation de la sévérité pour la région du Sud-Ouest en fonction des semaines épidémiologiques",
x = "Niveau",
y = "Semaine épidémiologique") +
theme_minimal() +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "bottom", # Positionner la légende en bas
legend.direction = "horizontal") +
facet_wrap(~region_34, scales = "free_y", ncol = 1)
Sévérité pour le Sud
ggplot(subset(llcholeraclean, region_34 == "Sud"),
aes(y = epiweek_date_notification_2, fill = factor(state_of_dehydratation_1_mild_2_moderate_3_severe_16))) +
geom_bar(stat = "count", position = "stack") +
scale_fill_manual(values = c("1" = "green", "2" = "yellow", "3" = "red"),
name = "Niveau de sévérité",
labels = c("1" = "Légère", "2" = "Modérée", "3" = "Sévère")) +
labs(title = "Variation de la sévérité pour la région du Sud en fonction des semaines épidémiologiques",
x = "Niveau",
y = "Semaine épidémiologique") +
theme_minimal() +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "bottom", # Positionner la légende en bas
legend.direction = "horizontal") +
facet_wrap(~region_34, scales = "free_y", ncol = 1)
Repartition des Cas et Deces par région en 2023
I-Creons en primaire une colonne Cas dans notre jeu de donnée
llcholeraclean <- llcholeraclean %>%
mutate(cas = ifelse(!is.na(n_id_1), 1, 0))Convertir les cas et deces en numeric pour pouvoir afficher
llcholeraclean$cas <- as.numeric(as.character(llcholeraclean$cas))
llcholeraclean$deces <- as.numeric(as.character(llcholeraclean$deces))## Warning: NAs introduced by coercioncopnvertir pour enlever les valeurs manquante par 0
llcholeraclean$cas[is.na(llcholeraclean$cas)] <- 0
llcholeraclean$deces[is.na(llcholeraclean$deces)] <- 0##Courbe representative des cas (regions: centre, littoral, sud, sud-Ouet et Ouest)
llcholeraclean %>%
filter(!(region_34 %in% c("Est", "Nord-Ouest"))) %>%
ggplot() +
aes(x = epiweek_date_notification_2, fill = region_34) +
geom_bar() +
scale_fill_manual(values = c(Centre = "red",
Est = "#BD9A00", Littoral = "#31B425", `Nord-Ouest` = "#00C19F", Ouest = "blue", Sud = "#B280FC" ,
`Sud-Ouest` = "#FF61C3" )) +
labs(x = "Semaine épidémiologique", y = "Nombre de décès ", title = " Évolution des décès au cours de l'année 2023",
fill = "Régions") +
theme_minimal() +
theme(plot.title = element_text(size = 20L, face = "bold",
hjust = 0.5)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
facet_grid(vars(deces))
L’évolution des cas et décès dans les 10 régions du Cameroun montre des
disparités significatives. La région du Centre se démarque avec le plus
grand nombre de décès, totalisant 159 pour l’année 2023, et un taux de
létalité de 3,4%. Cela souligne la nécessité de mesures renforcées dans
cette région pour contenir la situation. Le littoral suit avec 31 décès
et un taux de létalité de 2,1%. Cependant, le Sud, bien que présentant
un nombre moindre de décès (9), affiche un taux de létalité élevé de
9,5%, soulignant une situation plus critique compte tenu de sa
population de 953 923 habitants. Cette variation souligne l’importance
d’analyses approfondies pour comprendre les différences dans la
mortalité et adapter les stratégies en conséquence.
SITUATION DES DECES DES REGIONS CENTRE ET LITTORAL EN FOCTION DE L’ADMISSION EN HOSPITALISATION
Du fait que, d’après le graphe ci dessus, les deux regions ayants le taux de letalité le plus elevé sont le Centre et Littoral, ressortont pour chacun leur histogramme en fonction de l’admission en hospitalisation ou pas
1- verifions s’il y’a des valeurs autres que “oui” et “non” dans la colonne “hospitalisation_oui_non”
unique(llcholeraclean$hospitalisation_oui_non)## [1] "Oui" "OUI" NA "NON"
## [5] "Non" "non" "oui" "0"
## [9] "ARIVEE DECEDE" "YES" "NO" "oUI"
## [13] "yes"2- Remplacons les intrus par les correspondants initiaux( soit ‘oui’ ou “non”)
llcholeraclean <- llcholeraclean %>%
mutate(hospitalisation_oui_non = case_when(
hospitalisation_oui_non %in% c("OUI", "oUI", "yes", "YES", "Oui") ~ "oui",
TRUE ~ hospitalisation_oui_non
))
llcholeraclean <- llcholeraclean %>%
mutate(hospitalisation_oui_non = case_when(
hospitalisation_oui_non %in% c("NO", "Non", "NON", "0") ~ "non",
TRUE ~ hospitalisation_oui_non
))3-Convertir les valeurs manquantes par “0”
llcholeraclean$hospitalisation_oui_non[is.na(llcholeraclean$hospitalisation_oui_non)] <- 04-Puis remplacer “0” par “non”
llcholeraclean <- llcholeraclean %>%
mutate(hospitalisation_oui_non = ifelse(
hospitalisation_oui_non == "0", "non", hospitalisation_oui_non))5- Remplacer “ARIVEEDECEDE” par “non” dans la colonne “hospitalisation_oui_non”
llcholeraclean <- llcholeraclean %>%
mutate(hospitalisation_oui_non = ifelse(
hospitalisation_oui_non == "ARIVEE DECEDE", "non", hospitalisation_oui_non))6- Tracé de ladite courbe
llcholeraclean %>%
filter(region_34 %in% c("Centre", "Littoral")) %>%
ggplot() +
aes(
x = epiweek_date_notification_2,
fill = hospitalisation_oui_non
) +
geom_bar() +
scale_fill_hue(direction = 1) +
labs(
x = "Semaines épidémiologiques",
y = "Nombre de décès",
title = " Cumul d'évolution des décès dans les Régions: centre et Littoral",
subtitle = "Répartition des hospitalisés ou pas "
) +
theme_minimal() +
theme(
plot.title = element_text(size = 20L,
face = "bold",
hjust = 0.5),
plot.subtitle = element_text(size = 12L,
face = "bold"),
axis.title.y = element_text(size = 12L,
face = "bold"),
axis.title.x = element_text(size = 12L,
))+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
facet_grid(vars(deces)) 
importer la base pour les vulnerabilités wash et nettoyer les noms pour enlever les espaces (enquête evidences base cholera dans les regions Centre et Littoeral
ebasecholera <- import(here("ebasecholera.xlsx")) %>%
clean_names()## New names:
## • `Préciser` -> `Préciser...7`
## • `5. AIRE DE SANTÉ` -> `5. AIRE DE SANTÉ...8`
## • `5. AIRE DE SANTÉ` -> `5. AIRE DE SANTÉ...9`
## • `5. AIRE DE SANTÉ` -> `5. AIRE DE SANTÉ...10`
## • `5. AIRE DE SANTÉ` -> `5. AIRE DE SANTÉ...11`
## • `5. AIRE DE SANTÉ` -> `5. AIRE DE SANTÉ...12`
## • `5. AIRE DE SANTÉ` -> `5. AIRE DE SANTÉ...13`
## • `5. AIRE DE SANTÉ` -> `5. AIRE DE SANTÉ...14`
## • `1.2. Par quel canal?` -> `1.2. Par quel canal?...42`
## • `Preciser` -> `Preciser...88`
## • `Préciser` -> `Préciser...102`
## • `Préciser` -> `Préciser...119`
## • `5. AIRE DE SANTÉ` -> `5. AIRE DE SANTÉ...141`
## • `5. AIRE DE SANTÉ` -> `5. AIRE DE SANTÉ...142`
## • `1.2. Par quel canal?` -> `1.2. Par quel canal?...143`
## • `_id` -> `_id...144`
## • `_uuid` -> `_uuid...145`
## • `_submission_time` -> `_submission_time...146`
## • `_validation_status` -> `_validation_status...147`
## • `_notes` -> `_notes...148`
## • `_status` -> `_status...149`
## • `_submitted_by` -> `_submitted_by...150`
## • `__version__` -> `__version__...151`
## • `_tags` -> `_tags...152`
## • `_index` -> `_index...153`
## • `Environnement du ménage/Présence d’eaux usées` -> `Environnement du
## ménage/Présence d’eaux usées...168`
## • `Environnement du ménage/Présence d’eaux usées` -> `Environnement du
## ménage/Présence d’eaux usées...169`
## • `Preciser` -> `Preciser...179`
## • `Preciser` -> `Preciser...188`
## • `Preciser` -> `Preciser...190`
## • `Preciser` -> `Preciser...192`
## • `_id` -> `_id...197`
## • `_uuid` -> `_uuid...198`
## • `_submission_time` -> `_submission_time...199`
## • `_validation_status` -> `_validation_status...200`
## • `_notes` -> `_notes...201`
## • `_status` -> `_status...202`
## • `_submitted_by` -> `_submitted_by...203`
## • `__version__` -> `__version__...204`
## • `_tags` -> `_tags...205`
## • `_index` -> `_index...206`summary(ebasecholera)## x1_numero x2_date x3_region
## Length:1459 Min. :2023-07-18 00:00:00.0 Length:1459
## Class :character 1st Qu.:2023-07-20 00:00:00.0 Class :character
## Mode :character Median :2023-07-21 00:00:00.0 Mode :character
## Mean :2023-07-20 14:45:25.0
## 3rd Qu.:2023-07-21 00:00:00.0
## Max. :2023-07-24 00:00:00.0
## NA's :706
## x4_district_de_sante aire_de_sante_djoungolo aire_de_sante_efoulan
## Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character
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##
##
##
##
## preciser_7 x5_aire_de_sante_8 x5_aire_de_sante_9 x5_aire_de_sante_10
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
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##
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## Class :character Class :character Class :character
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##
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## 1st Qu.:3.848 1st Qu.:11.50
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## Mean :3.872 Mean :11.52
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## Min. :-213.7 Min. : 3.0 Min. : 18.0
## 1st Qu.: 0.0 1st Qu.: 20.0 1st Qu.: 29.0
## Median : 728.8 Median : 77.6 Median : 33.0
## Mean : 520.8 Mean : 662.9 Mean : 49.6
## 3rd Qu.: 755.9 3rd Qu.:1600.0 3rd Qu.: 40.0
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## Max. :1.0000
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## Median :1.000
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## x6_comment_reconnaissez_vous_les_cas_suspects_de_cholera_jaunisse
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## x6_comment_reconnaissez_vous_les_cas_suspects_de_cholera_diarrhee
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## x6_comment_reconnaissez_vous_les_cas_suspects_de_cholera_deshydratation
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## x6_comment_reconnaissez_vous_les_cas_suspects_de_cholera_vomissements
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## x6_comment_reconnaissez_vous_les_cas_suspects_de_cholera_nsp
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## x8_quelles_mesures_doit_on_prendre_pour_eviter_de_contracter_le_cholera_laver_les_mains
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## x8_quelles_mesures_doit_on_prendre_pour_eviter_de_contracter_le_cholera_laver_les_habits
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## x8_quelles_mesures_doit_on_prendre_pour_eviter_de_contracter_le_cholera_boire_la_doxycycline
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## x8_quelles_mesures_doit_on_prendre_pour_eviter_de_contracter_le_cholera_desinfecter_les_puits
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## x8_quelles_mesures_doit_on_prendre_pour_eviter_de_contracter_le_cholera_lavage_des_aliments
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## x8_quelles_mesures_doit_on_prendre_pour_eviter_de_contracter_le_cholera_lavage_du_sol_a_leau_de_javel
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## x14_quelles_sont_les_mesures_additionnelles_a_mettre_en_place_autour_dun_cas_sensibilisation_de_proximite
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## x14_quelles_sont_les_mesures_additionnelles_a_mettre_en_place_autour_dun_cas_chimioprophylaxie_dans_tout_le_quartier
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## x1_que_faites_vous_devant_un_cas_suspect_de_cholera_je_lui_donne_urgemment_de_la_doxycycline
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## x1_que_faites_vous_devant_un_cas_suspect_de_cholera_je_l_emmene_dans_une_salle_loin_du_reste_des_malades
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## x1_que_faites_vous_devant_un_cas_suspect_de_cholera_je_le_refere_dans_une_autre_fosa
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## x1_que_faites_vous_devant_un_cas_suspect_de_cholera_je_ne_le_touche_pas_il_est_contagieux
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## x1_que_faites_vous_devant_un_cas_suspect_de_cholera_nsp
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## x1_que_faites_vous_devant_un_cas_suspect_de_cholera_autre preciser_102
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## x2_qui_informez_vous_de_la_presence_d_un_cas_suspect_de_cholera_le_chef_de_ds
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## x2_qui_informez_vous_de_la_presence_d_un_cas_suspect_de_cholera_le_chef_de_laire
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## x2_qui_informez_vous_de_la_presence_d_un_cas_suspect_de_cholera_01_personnel_de_la_dlmep
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## x2_qui_informez_vous_de_la_presence_d_un_cas_suspect_de_cholera_le_cpc
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## x2_qui_informez_vous_de_la_presence_d_un_cas_suspect_de_cholera_personne_je_soigne_le_malade
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## x2_qui_informez_vous_de_la_presence_d_un_cas_suspect_de_cholera_tous
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## x2_qui_informez_vous_de_la_presence_d_un_cas_suspect_de_cholera_nsp
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## x4_devant_un_cas_de_cholera_vous_devez_vous_assurer_que_les_mesures_ci_apres_soient_prises_autour_du_cas_desinfecter_le_menage
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## x4_devant_un_cas_de_cholera_vous_devez_vous_assurer_que_les_mesures_ci_apres_soient_prises_autour_du_cas_chercher_la_source
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## x4_devant_un_cas_de_cholera_vous_devez_vous_assurer_que_les_mesures_ci_apres_soient_prises_autour_du_cas_chercher_les_cas_additionnels
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## x4_devant_un_cas_de_cholera_vous_devez_vous_assurer_que_les_mesures_ci_apres_soient_prises_autour_du_cas_autre
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## x5_pendant_une_epidemie_de_cholera_comment_doit_on_organiser_les_services_de_l_hopital_le_triage_doit_etre_organise_apres_la_consultation_des_malades
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## x5_pendant_une_epidemie_de_cholera_comment_doit_on_organiser_les_services_de_l_hopital_le_triage_n_est_pas_necessaire
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## x5_pendant_une_epidemie_de_cholera_comment_doit_on_organiser_les_services_de_l_hopital_le_triage_doit_etre_organise_des_l_entree
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## x4_quel_est_traitement_antibiotique_qui_peut_etre_utilise_pour_traiter_les_cas_de_cholera_metronidazole
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## x4_quel_est_traitement_antibiotique_qui_peut_etre_utilise_pour_traiter_les_cas_de_cholera_azithromycine
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## x4_quel_est_traitement_antibiotique_qui_peut_etre_utilise_pour_traiter_les_cas_de_cholera_doxycycline
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## Length:1459 Mode:logical Min. : 1.0 Length:1459
## Class :character NA's:1459 1st Qu.:198.2 Class :character
## Mode :character Median :395.5 Mode :character
## Mean :395.5
## 3rd Qu.:592.8
## Max. :790.0
## NA's :669
## record_your_current_location_latitude record_your_current_location_longitude
## Min. :3.815 Min. : 9.630
## 1st Qu.:3.890 1st Qu.: 9.726
## Median :4.026 Median : 9.753
## Mean :3.989 Mean :10.434
## 3rd Qu.:4.051 3rd Qu.:11.512
## Max. :4.172 Max. :11.567
## NA's :37 NA's :37
## record_your_current_location_altitude record_your_current_location_precision
## Min. : -67.83 Min. : 0.757
## 1st Qu.: 19.60 1st Qu.: 4.266
## Median : 36.65 Median : 4.905
## Mean : 273.31 Mean : 185.652
## 3rd Qu.: 730.81 3rd Qu.: 12.779
## Max. :1069.81 Max. :8993.296
## NA's :37 NA's :37
## situation_sociogeographique_du_menage
## Length:1459
## Class :character
## Mode :character
##
##
##
##
## situation_sociogeographique_du_menage_marecage
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.285
## 3rd Qu.:1.000
## Max. :1.000
## NA's :10
## situation_sociogeographique_du_menage_colline
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.1567
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :10
## situation_sociogeographique_du_menage_quartier_precaire
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.4734
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :10
## situation_sociogeographique_du_menage_logement_collectif_sic
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.1429
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :10
## situation_sociogeographique_du_menage_logement_individuel
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.332
## 3rd Qu.:1.000
## Max. :1.000
## NA's :10
## situation_sociogeographique_du_menage_cour_commune environnement_du_menage
## Min. :0.0000 Length:1459
## 1st Qu.:0.0000 Class :character
## Median :0.0000 Mode :character
## Mean :0.2443
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :10
## environnement_du_menage_presence_de_rigoles
## Min. :0.0000
## 1st Qu.:0.0000
## Median :1.0000
## Mean :0.6355
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :46
## environnement_du_menage_presence_d_eaux_usees_168
## Min. :0.0000
## 1st Qu.:0.0000
## Median :1.0000
## Mean :0.5265
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :46
## environnement_du_menage_presence_d_eaux_usees_169
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.3227
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :46
## environnement_du_menage_presence_de_dechets_menagers
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.4636
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :46
## environnement_du_menage_promiscuite localisation_du_menage
## Min. :0.0000 Length:1459
## 1st Qu.:0.0000 Class :character
## Median :0.0000 Mode :character
## Mean :0.2633
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :46
## localisation_du_menage_pres_d_un_marche localisation_du_menage_lieu_spontane
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000
## Mean :0.1392 Mean :0.5706
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :29 NA's :29
## localisation_du_menage_grand_carrefour
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.2021
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :29
## localisation_du_menage_pres_d_un_espace_de_depot_hysacam
## Min. :0.00000
## 1st Qu.:0.00000
## Median :0.00000
## Mean :0.06643
## 3rd Qu.:0.00000
## Max. :1.00000
## NA's :29
## localisation_du_menage_autre localisation_du_menage_option_6
## Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000
## Mean :0.1441 Mean :0.03217
## 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.00000
## NA's :29 NA's :29
## preciser_179 etat_de_la_poubelle_au_moment_de_l_enquete
## Length:1459 Length:1459
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## espace_autour_de_la_poubelle types_de_dechets_deverses
## Length:1459 Length:1459
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## types_de_dechets_deverses_restes_de_nourriture
## Min. :0.0000
## 1st Qu.:1.0000
## Median :1.0000
## Mean :0.8298
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :72
## types_de_dechets_deverses_appareils_et_fournitures_diverses
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.3973
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :72
## types_de_dechets_deverses_vetements_et_assimiles
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.434
## 3rd Qu.:1.000
## Max. :1.000
## NA's :72
## types_de_dechets_deverses_couches_et_autres_dechets_assimiles
## Min. :0.0000
## 1st Qu.:0.0000
## Median :1.0000
## Mean :0.5688
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :72
## types_de_dechets_deverses_autre preciser_188
## Min. :0.00000 Length:1459
## 1st Qu.:0.00000 Class :character
## Median :0.00000 Mode :character
## Mean :0.09229
## 3rd Qu.:0.00000
## Max. :1.00000
## NA's :72
## existe_t_il_un_point_d_eau_dans_le_menage preciser_190
## Length:1459 Length:1459
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## existe_t_il_des_toilettes_dans_le_menage preciser_192 preciser_latrine
## Length:1459 Length:1459 Min. :0.0000
## Class :character Class :character 1st Qu.:0.0000
## Mode :character Mode :character Median :1.0000
## Mean :0.5372
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :20
## preciser_toilette_a_fosse_septique position_des_toilettes
## Min. :0.0000 Length:1459
## 1st Qu.:0.0000 Class :character
## Median :1.0000 Mode :character
## Mean :0.5177
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :20
## distance_estimee_entre_le_puits_forage_et_les_toilettes id_197
## Min. : 0.00 Min. :253044658
## 1st Qu.: 5.00 1st Qu.:253266793
## Median : 10.00 Median :253553927
## Mean : 69.69 Mean :253698066
## 3rd Qu.: 30.00 3rd Qu.:253999802
## Max. :3000.00 Max. :255236846
## NA's :192
## uuid_198 submission_time_199 validation_status_200
## Length:1459 Min. :2023-07-15 12:11:03.00 Mode:logical
## Class :character 1st Qu.:2023-07-16 16:32:06.50 NA's:1459
## Mode :character Median :2023-07-17 19:09:56.00
## Mean :2023-07-18 06:46:26.22
## 3rd Qu.:2023-07-19 11:22:23.50
## Max. :2023-07-24 14:33:59.00
##
## notes_201 status_202 submitted_by_203 version_204
## Mode:logical Length:1459 Length:1459 Length:1459
## NA's:1459 Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## tags_205 index_206
## Mode:logical Min. : 1.0
## NA's:1459 1st Qu.: 365.5
## Median : 730.0
## Mean : 730.0
## 3rd Qu.:1094.5
## Max. :1459.0
## unique(ebasecholera$x2_connaissez_vous_la_definition_des_cas_du_cholera)## [1] "Non" "Oui" NARemplacer les valeurs manquantes de la colonne “x2_connaissez_vous_la_definition_des_cas_du_cholera” par 0
ebasecholera$x2_connaissez_vous_la_definition_des_cas_du_cholera[is.na(ebasecholera$x2_connaissez_vous_la_definition_des_cas_du_cholera)] <- 0Puis remplacer 0 par non
ebasecholera <- ebasecholera %>%
mutate( x2_connaissez_vous_la_definition_des_cas_du_cholera = ifelse(
x2_connaissez_vous_la_definition_des_cas_du_cholera == "non", "Non", x2_connaissez_vous_la_definition_des_cas_du_cholera))verifier les valeurs
unique(ebasecholera$x3_region)## [1] "Centre" NA1. Verification si definition des cas correcte ou pas, mais avant, remplacer les valeurs manquantes par 0, puis 0 par Non, pusi faire le graphe
ebasecholera$definition_des_cas_corrects_ou_pas[is.na(ebasecholera$definition_des_cas_corrects_ou_pas)] <- 0
ebasecholera <- ebasecholera %>%
mutate( definition_des_cas_corrects_ou_pas = ifelse(definition_des_cas_corrects_ou_pas == "0", "Non", definition_des_cas_corrects_ou_pas))
ebasecholera %>%
filter(is.na(submitted_by_203)) %>%
ggplot() +
aes(
x = definition_des_cas_corrects_ou_pas,
fill = x3_region
) +
labs( x = "Reponce pour la connaissance",
y = "niveau",
fill = "Régions",
title = "Verification de la definition des cas")+
geom_bar() +
scale_fill_hue(direction = 1) +
theme_minimal()
summary(llcholera110124)## n_id_1 epiweek_date_notification_2 epiweek_date_symptoms_3
## Length:21304 Length:21304 Length:21304
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## names_and_surnames phone_number age_year_6 sex_7
## Length:21304 Length:21304 Min. : 0.00 Length:21304
## Class :character Class :character 1st Qu.: 18.00 Class :character
## Mode :character Mode :character Median : 28.00 Mode :character
## Mean : 29.85
## 3rd Qu.: 40.00
## Max. :103.00
## NA's :226
## profession village_quarter health_area_of_origin
## Length:21304 Length:21304 Length:21304
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## health_district_of_origin health_area_notifying health_district_notifying_13
## Length:21304 Length:21304 Length:21304
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## diarrhoea_oui_non_14 vomitting_oui_non_15
## Length:21304 Length:21304
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## state_of_dehydratation_1_mild_2_moderate_3_severe_16
## Min. :1.000
## 1st Qu.:2.000
## Median :2.000
## Mean :2.062
## 3rd Qu.:3.000
## Max. :4.000
## NA's :1000
## date_of_onset_of_symptoms_17 date_of_consultation_18
## Min. :2021-10-15 00:00:00.00 Min. :2021-10-16 00:00:00.00
## 1st Qu.:2022-04-04 00:00:00.00 1st Qu.:2022-04-05 00:00:00.00
## Median :2022-07-19 00:00:00.00 Median :2022-07-21 00:00:00.00
## Mean :2022-10-22 08:19:10.43 Mean :2022-09-18 22:35:58.68
## 3rd Qu.:2023-04-29 00:00:00.00 3rd Qu.:2023-05-01 00:00:00.00
## Max. :3034-12-03 00:00:00.00 Max. :2024-01-09 00:00:00.00
## NA's :1
## date_of_notification_19 hospitalisation_oui_non
## Min. :2021-01-03 00:00:00.00 Length:21304
## 1st Qu.:2022-04-05 00:00:00.00 Class :character
## Median :2022-07-22 00:00:00.00 Mode :character
## Mean :2022-09-19 05:30:27.71
## 3rd Qu.:2023-05-01 00:00:00.00
## Max. :2024-01-09 00:00:00.00
##
## date_of_hospitalisation site_of_case_management treatment_ors_oui_no
## Length:21304 Length:21304 Length:21304
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## treatment_antibiotic_oui_no treatment_iv_liquid_oui_no treatment_zinc_oui_no
## Length:21304 Length:21304 Length:21304
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## date_of_sample_collection result_of_rdt culture_oui_no
## Length:21304 Length:21304 Length:21304
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## result_of_culture date_of_discharge outcome_2_healed_3_dead_32
## Length:21304 Length:21304 Length:21304
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## observations_facteur_de_risque_1_eau_2_assainissement_3_hygiene_4_autres_saison_surpeuplement_deplacement
## Length:21304
## Class :character
## Mode :character
##
##
##
##
## region_34 commentaires_35 commentaires_36 statut_vaccinal
## Length:21304 Length:21304 Mode:logical Length:21304
## Class :character Class :character NA's:21304 Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## x38 x39 facteurs_risque_eau_1
## Mode:logical Mode:logical Mode:logical
## NA's:21304 NA's:21304 NA's:21304
##
##
##
##
##
## facteurs_risque_assainissements_2 facteurs_risque_hygiene_3
## Mode:logical Mode:logical
## NA's:21304 NA's:21304
##
##
##
##
##
## facteurs_risque_autre_4 ds_origine_44 as_origine_45 x46
## Mode:logical Length:21304 Length:21304 Mode:logical
## NA's:21304 Class :character Class :character NA's:21304
## Mode :character Mode :character
##
##
##
##
## x47 n_id_48 epiweek_date_notification_49
## Mode:logical Mode:logical Mode:logical
## NA's:21304 NA's:21304 NA's:21304
##
##
##
##
##
## epiweek_date_symptoms_50 age_year_51 sex_52 as_origine_53
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:21304 NA's:21304 NA's:21304 NA's:21304
##
##
##
##
##
## ds_origine_54 health_district_notifying_55 diarrhoea_oui_non_56
## Mode:logical Mode:logical Mode:logical
## NA's:21304 NA's:21304 NA's:21304
##
##
##
##
##
## vomitting_oui_non_57 state_of_dehydratation_1_mild_2_moderate_3_severe_58
## Mode:logical Mode:logical
## NA's:21304 NA's:21304
##
##
##
##
##
## date_of_onset_of_symptoms_59 date_of_consultation_60 date_of_notification_61
## Mode:logical Mode:logical Mode:logical
## NA's:21304 NA's:21304 NA's:21304
##
##
##
##
##
## outcome_2_healed_3_dead_62 region_63
## Mode:logical Mode:logical
## NA's:21304 NA's:21304
##
##
##
##
## faire le tableau de synthèse (avec region, district le nombre de fois qu’un district a notifié en fonction de la sémaine epi, ainsi que le nombre de cas rapporté pendant la semaine) pour l’année 2023(llcholeraclean) avec nom lltableau23 et 2021, 2022 (llcholera1104244) avec nom ll tableau 2122
library(dplyr)
lltableau23 <- llcholeraclean %>%
clean_names() %>%
group_by(region_34, health_district_notifying_13, epiweek_date_notification_2) %>%
summarize(
nombre_semaines_notifiées = n_distinct(epiweek_date_notification_2),
nombre_de_cas = n()
) %>%
arrange(region_34, health_district_notifying_13, epiweek_date_notification_2)## `summarise()` has grouped output by 'region_34',
## 'health_district_notifying_13'. You can override using the `.groups` argument.lltableau21_22 <- llcholera110124 %>%
clean_names() %>%
group_by(region_34, date_of_notification_19, health_district_notifying_13, epiweek_date_notification_2) %>%
summarize(
nombre_semaines_notifiées = n_distinct(epiweek_date_notification_2),
nombre_de_cas = n()
) %>%
arrange(region_34, health_district_notifying_13, epiweek_date_notification_2)## `summarise()` has grouped output by 'region_34', 'date_of_notification_19',
## 'health_district_notifying_13'. You can override using the `.groups` argument.Exportation
library(openxlsx)
write.xlsx(lltableau21_22, "synthese_totale.xlsx", rowNames = FALSE)importer les données de population et taux d’attaque
tableurtaux <- import(here("tableurtaux.xlsx")) %>%
clean_names()
summary(tableurtaux)## region date_de_notification district_de_notification
## Length:4473 Min. :2021-01-03 00:00:00.00 Length:4473
## Class :character 1st Qu.:2022-04-24 00:00:00.00 Class :character
## Mode :character Median :2022-08-23 00:00:00.00 Mode :character
## Mean :2022-10-21 08:21:53.48
## 3rd Qu.:2023-05-14 00:00:00.00
## Max. :2024-01-09 00:00:00.00
##
## semaine_epi nombrede_semaines_notifiees nombre_de_cas
## Length:4473 Min. :1 Min. : 1.000
## Class :character 1st Qu.:1 1st Qu.: 1.000
## Mode :character Median :1 Median : 2.000
## Mean :1 Mean : 4.516
## 3rd Qu.:1 3rd Qu.: 5.000
## Max. :1 Max. :225.000
##
## populations_ds populations_re_g taux_dattaque_ds taux_dattaque_re_g
## Min. : 17901 Min. :1990710 Min. :0.0001219 Min. :1.899e-05
## 1st Qu.:188293 1st Qu.:4476094 1st Qu.:0.0003122 1st Qu.:1.564e-04
## Median :395315 Median :4476094 Median :0.0007038 Median :5.347e-04
## Mean :386481 Mean :4247391 Mean :0.0020607 Mean :1.674e-03
## 3rd Qu.:575410 3rd Qu.:5266094 3rd Qu.:0.0018261 3rd Qu.:1.405e-03
## Max. :820017 Max. :5266094 Max. :0.1044519 Max. :1.045e-01
## NA's :1 NA's :1Calculer la mediane et la representer
mediane <- tapply(tableurtaux$taux_dattaque_ds, tableurtaux$district_de_notification, median)representation de la médiane
barplot(mediane, names.arg = names(mediane), xlab = "Districts", ylab = "Médiane du taux d'attaque")
Calculons la médiane des populations
medianepop <- tapply(tableurtaux$populations_ds, tableurtaux$district_de_notification, median)Representation de la médiane taux d’attaque et populations en focnction des districts
medianetauxpop <- aggregate(cbind(taux_dattaque_ds, populations_ds) ~ district_de_notification, tableurtaux, median)
print(medianetauxpop)## district_de_notification taux_dattaque_ds populations_ds
## 1 ABO 0.0032494487 30774.45
## 2 awae 0.0067041654 29832.20
## 3 BAFIA 0.0014050458 177930.14
## 4 Bakassi 0.0080235401 37389.98
## 5 BANGUE 0.0005059251 395315.42
## 6 BIYEM ASSI 0.0005298527 377463.39
## 7 BIYEM-ASSI 0.0002649264 377463.39
## 8 BOKO 0.0002673915 373983.44
## 9 BONASSAMA 0.0005278258 568369.33
## 10 Buea 0.0007966302 188293.13
## 11 BUEA 0.0026554341 188293.13
## 12 CITE VERTE 0.0002438974 820016.81
## 13 CITE_DES_PALMIERS 0.0002438974 820016.81
## 14 DEIDO 0.0004683052 640607.91
## 15 DIBOMBARI 0.0022863432 43737.97
## 16 DJOUNGOLO 0.0005213677 575409.60
## 17 Ebebda 0.0127103748 23602.77
## 18 EDEA 0.0005448585 183533.90
## 19 EFOULAN 0.0004165733 480107.53
## 20 Ekondo Titi 0.0114695661 61031.08
## 21 ELIGM FOMO 0.0055861561 17901.40
## 22 JAPOMA 0.0005347423 187005.97
## 23 KUMBA NORTH 0.0007281580 137332.83
## 24 KUMBA SOUTH 0.0011115331 269897.49
## 25 Limbe 0.0018569221 215410.22
## 26 LIMBE 0.0037138442 215410.22
## 27 LOGBABA 0.0007144824 279922.92
## 28 LOUM 0.0013872576 72084.66
## 29 MAMFE 0.0009256517 108032.00
## 30 MANOKA 0.0032205727 31050.38
## 31 MBALMAYO 0.0007548100 132483.67
## 32 MBANGA 0.0011899026 84040.49
## 33 MBANKOMO 0.0029315093 34112.12
## 34 Mbonge 0.0010204146 97999.38
## 35 MELONG 0.0008853779 112946.12
## 36 MFOU 0.0008911837 112210.31
## 37 Monatele 0.0018704338 53463.53
## 38 MUNDEMBA 0.0042979620 23266.84
## 39 MUYUKA 0.0016240483 123149.05
## 40 MVOG ADA 0.0004854741 411968.38
## 41 NEW_BELL 0.0006169628 324168.68
## 42 NJOMBE_PENJA 0.0067511107 59249.51
## 43 NKOLBISSON 0.0010392857 192439.87
## 44 NKOLNDONGO 0.0003334479 599793.91
## 45 NKONGSAMBA 0.0016406606 121902.12
## 46 NTUI 0.0007071521 141412.29
## 47 NYLON 0.0004321381 462814.97
## 48 OBALA 0.0013554651 147550.83
## 49 ODZA 0.0002258430 442785.52
## 50 OKOLA 0.0016531824 60489.39
## 51 Saa 0.0012993647 76960.69
## 52 SOA 0.0040511362 49368.87
## 53 Tiko 0.0024348054 164284.18
## 54 TOMBEL 0.0011669295 85694.98re-calculer la mediane de pop du DS
medianPop <- median(tableurtaux$populations_ds, na.rm = TRUE)
print(medianPop)## [1] 395315.4Representation graphique de la mediane
plot(tableurtaux$populations_ds, tableurtaux$taux_dattaque_ds, main = "Médiane en fonction des Populations",
xlab = "Population", ylab = "Taux d'attaque")
abline(h = medianPop, col = "red", lty = 2, new = TRUE)
llcholeraclean <- llcholeraclean %>%
mutate(sex_7 = ifelse(sex_7 == "H", "male", sex_7)) %>%
mutate(sex_7 = ifelse(sex_7 == "F", "female", sex_7))
llcholeraclean$sex_7[is.na(llcholeraclean$sex_7)] <- 0
llcholeraclean <- llcholeraclean %>%
mutate(sex_7 =ifelse(sex_7 =="0", "male", sex_7))
llcholeraclean <- llcholeraclean %>%
mutate(state_of_dehydratation_1_mild_2_moderate_3_severe_16 = ifelse(state_of_dehydratation_1_mild_2_moderate_3_severe_16 == "4", "3", state_of_dehydratation_1_mild_2_moderate_3_severe_16))
llcholeraclean$age_year_6[is.na(llcholeraclean$age_year_6)] <- 28# Transformer les données pour créer une pyramide des âges
llcholeraclean <- transform(llcholeraclean,
age_group = cut(age_year_6, breaks = seq(0, 100, by = 10), labels = seq(0, 95, by = 10)))
# Création du graphique
ggplot(data = llcholeraclean) +
geom_bar(data = subset(llcholeraclean, sex_7 == "male"), aes(x = age_group, y = ..count.., fill = sex_7), position = "dodge") +
geom_bar(data = subset(llcholeraclean, sex_7 == "female"), aes(x = age_group, y = -..count.., fill = sex_7), position = "dodge") +
facet_grid(. ~ state_of_dehydratation_1_mild_2_moderate_3_severe_16) +
labs(title = "Pyramide des âges en fonction du niveau de sévérité du choléra",
x = "Âge",
y = "Nombre de cas",
fill = "Sexe") +
scale_y_continuous(labels = abs, breaks = seq(-90, 175, by = 40)) +
# Pour afficher les valeurs absolues sur l'axe y
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=2)) ## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Pour faire pivoter les étiquettes d'âgeModélisation de la persistance
Étape 1 : Préparation des données Assurez-vous que vos données sont chargées dans R et qu’elles sont prêtes pour l’analyse.
Étape 2 : Modélisation de la persistance temporelle Utilisez un modèle de régression pour examiner la relation entre le nombre de cas de choléra et le temps (en semaines épidémiologiques) pour chaque région. Vous pouvez utiliser la fonction lm() pour ajuster un modèle linéaire. #1 Centre
donnee_centre <- subset(llcholeraclean, region_34 == "Centre")
modele_centre <- lm(cas ~ epiweek_date_notification_2, data = donnee_centre)
donnee_centre$epiweek_date_notification_2 <- as.numeric(donnee_centre$epiweek_date_notification_2)## Warning: NAs introduced by coercionsummary(modele_centre)##
## Call:
## lm(formula = cas ~ epiweek_date_notification_2, data = donnee_centre)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9865 0.0000 0.0000 0.0000 0.5000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.000e+00 1.118e-02 89.413 <2e-16 ***
## epiweek_date_notification_2S04_2023 9.056e-13 1.370e-02 0.000 1.000
## epiweek_date_notification_2S05_2023 9.055e-13 1.250e-02 0.000 1.000
## epiweek_date_notification_2S06_2023 9.056e-13 1.582e-02 0.000 1.000
## epiweek_date_notification_2S07_2023 9.055e-13 2.740e-02 0.000 1.000
## epiweek_date_notification_2S08_2023 9.055e-13 2.740e-02 0.000 1.000
## epiweek_date_notification_2S11_2023 9.056e-13 2.740e-02 0.000 1.000
## epiweek_date_notification_2S12_2023 9.056e-13 2.092e-02 0.000 1.000
## epiweek_date_notification_2S13_2023 9.056e-13 1.272e-02 0.000 1.000
## epiweek_date_notification_2S14_2023 9.056e-13 1.161e-02 0.000 1.000
## epiweek_date_notification_2S15_2023 9.056e-13 1.176e-02 0.000 1.000
## epiweek_date_notification_2S16_2023 9.056e-13 1.130e-02 0.000 1.000
## epiweek_date_notification_2S17_2023 9.056e-13 1.124e-02 0.000 1.000
## epiweek_date_notification_2S18_2023 9.055e-13 1.126e-02 0.000 1.000
## epiweek_date_notification_2S19_2023 9.055e-13 1.124e-02 0.000 1.000
## epiweek_date_notification_2S20_2023 9.056e-13 1.124e-02 0.000 1.000
## epiweek_date_notification_2S21_2023 9.056e-13 1.125e-02 0.000 1.000
## epiweek_date_notification_2S22_2023 9.056e-13 1.124e-02 0.000 1.000
## epiweek_date_notification_2S23_2023 9.055e-13 1.127e-02 0.000 1.000
## epiweek_date_notification_2S24_2023 9.055e-13 1.128e-02 0.000 1.000
## epiweek_date_notification_2S25_2023 9.057e-13 1.140e-02 0.000 1.000
## epiweek_date_notification_2S26_2023 9.056e-13 1.149e-02 0.000 1.000
## epiweek_date_notification_2S27_2023 9.058e-13 1.163e-02 0.000 1.000
## epiweek_date_notification_2S28_2023 9.056e-13 1.172e-02 0.000 1.000
## epiweek_date_notification_2S29_2023 9.058e-13 1.173e-02 0.000 1.000
## epiweek_date_notification_2S30_2023 9.056e-13 1.169e-02 0.000 1.000
## epiweek_date_notification_2S31_2023 9.059e-13 1.161e-02 0.000 1.000
## epiweek_date_notification_2S32_2023 9.056e-13 1.171e-02 0.000 1.000
## epiweek_date_notification_2S33_2023 9.057e-13 1.190e-02 0.000 1.000
## epiweek_date_notification_2S34_2023 9.057e-13 1.211e-02 0.000 1.000
## epiweek_date_notification_2S35_2023 9.056e-13 1.250e-02 0.000 1.000
## epiweek_date_notification_2S36_2023 9.054e-13 1.370e-02 0.000 1.000
## epiweek_date_notification_2S37_2023 -1.351e-02 1.156e-02 -1.169 0.242
## epiweek_date_notification_2S38_2023 9.056e-13 1.186e-02 0.000 1.000
## epiweek_date_notification_2S39_2023 9.055e-13 1.214e-02 0.000 1.000
## epiweek_date_notification_2S40_2023 9.057e-13 1.244e-02 0.000 1.000
## epiweek_date_notification_2S41_2023 9.055e-13 1.229e-02 0.000 1.000
## epiweek_date_notification_2S42_2023 9.059e-13 1.264e-02 0.000 1.000
## epiweek_date_notification_2S43_2023 9.057e-13 1.331e-02 0.000 1.000
## epiweek_date_notification_2S44_2023 9.058e-13 1.303e-02 0.000 1.000
## epiweek_date_notification_2S45_2023 9.052e-13 1.514e-02 0.000 1.000
## epiweek_date_notification_2S46_2023 9.049e-13 1.349e-02 0.000 1.000
## epiweek_date_notification_2S47_2023 9.053e-13 1.582e-02 0.000 1.000
## epiweek_date_notification_2S48_2023 -2.857e-01 1.464e-02 -19.512 <2e-16 ***
## epiweek_date_notification_2S49_2023 -5.000e-01 2.092e-02 -23.897 <2e-16 ***
## epiweek_date_notification_2S50_2023 9.050e-13 2.092e-02 0.000 1.000
## epiweek_date_notification_2S51_2023 9.050e-13 1.678e-02 0.000 1.000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02501 on 4661 degrees of freedom
## Multiple R-squared: 0.2706, Adjusted R-squared: 0.2634
## F-statistic: 37.59 on 46 and 4661 DF, p-value: < 2.2e-16plot(donnee_centre$epiweek_date_notification_2, donnee_centre$cas,
xlab = "Semaines épidémiologiques", ylab = "Nombre de cas",
main = "Tracé des cas de choléra par semaine (Centre)",
xlim = c(1, 52), ylim = range(donnee_centre$cas))
# Ajouter la ligne de régression
abline(modele_centre, col = "red")## Warning in abline(modele_centre, col = "red"): only using the first two of 47
## regression coefficients
#2 Littoral
donnee_littoral <- subset(llcholeraclean, region_34 == "Littoral")
modele_littoral <- lm(cas ~ epiweek_date_notification_2, data = donnee_littoral)
summary(modele_littoral)##
## Call:
## lm(formula = cas ~ epiweek_date_notification_2, data = donnee_littoral)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.570e-15 0.000e+00 0.000e+00 0.000e+00 3.791e-13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.000e+00 2.449e-15 4.083e+14 <2e-16
## epiweek_date_notification_2S02_2023 -4.134e-28 3.644e-15 0.000e+00 1.0000
## epiweek_date_notification_2S03_2023 -3.060e-28 3.907e-15 0.000e+00 1.0000
## epiweek_date_notification_2S04_2023 -4.029e-28 4.330e-15 0.000e+00 1.0000
## epiweek_date_notification_2S05_2023 -3.445e-28 3.517e-15 0.000e+00 1.0000
## epiweek_date_notification_2S06_2023 -5.893e-29 3.464e-15 0.000e+00 1.0000
## epiweek_date_notification_2S07_2023 -5.255e-29 3.907e-15 0.000e+00 1.0000
## epiweek_date_notification_2S08_2023 -3.507e-28 3.721e-15 0.000e+00 1.0000
## epiweek_date_notification_2S09_2023 -1.981e-28 3.371e-15 0.000e+00 1.0000
## epiweek_date_notification_2S10_2023 -1.844e-28 4.330e-15 0.000e+00 1.0000
## epiweek_date_notification_2S11_2023 -3.365e-28 4.163e-15 0.000e+00 1.0000
## epiweek_date_notification_2S12_2023 -2.994e-28 5.137e-15 0.000e+00 1.0000
## epiweek_date_notification_2S13_2023 -1.711e-28 3.807e-15 0.000e+00 1.0000
## epiweek_date_notification_2S14_2023 -3.824e-28 4.024e-15 0.000e+00 1.0000
## epiweek_date_notification_2S15_2023 -3.095e-28 3.464e-15 0.000e+00 1.0000
## epiweek_date_notification_2S16_2023 -3.076e-28 4.163e-15 0.000e+00 1.0000
## epiweek_date_notification_2S17_2023 -2.796e-28 3.415e-15 0.000e+00 1.0000
## epiweek_date_notification_2S18_2023 -2.082e-28 3.261e-15 0.000e+00 1.0000
## epiweek_date_notification_2S19_2023 1.820e-28 3.415e-15 0.000e+00 1.0000
## epiweek_date_notification_2S20_2023 -5.560e-28 3.015e-15 0.000e+00 1.0000
## epiweek_date_notification_2S21_2023 -5.081e-28 3.261e-15 0.000e+00 1.0000
## epiweek_date_notification_2S22_2023 -6.656e-28 3.517e-15 0.000e+00 1.0000
## epiweek_date_notification_2S23_2023 2.850e-29 4.330e-15 0.000e+00 1.0000
## epiweek_date_notification_2S24_2023 -1.744e-28 3.644e-15 0.000e+00 1.0000
## epiweek_date_notification_2S25_2023 -3.005e-28 4.024e-15 0.000e+00 1.0000
## epiweek_date_notification_2S26_2023 -1.741e-28 6.324e-15 0.000e+00 1.0000
## epiweek_date_notification_2S27_2023 -6.608e-28 4.535e-15 0.000e+00 1.0000
## epiweek_date_notification_2S28_2023 -2.090e-28 3.261e-15 0.000e+00 1.0000
## epiweek_date_notification_2S29_2023 -6.024e-28 4.163e-15 0.000e+00 1.0000
## epiweek_date_notification_2S30_2023 5.575e-15 2.734e-15 2.039e+00 0.0416
## epiweek_date_notification_2S31_2023 -2.596e-28 2.935e-15 0.000e+00 1.0000
## epiweek_date_notification_2S32_2023 -3.098e-28 2.835e-15 0.000e+00 1.0000
## epiweek_date_notification_2S33_2023 -1.405e-28 2.821e-15 0.000e+00 1.0000
## epiweek_date_notification_2S34_2023 -1.667e-28 2.583e-15 0.000e+00 1.0000
## epiweek_date_notification_2S35_2023 -2.308e-28 2.614e-15 0.000e+00 1.0000
## epiweek_date_notification_2S36_2023 -2.924e-28 2.657e-15 0.000e+00 1.0000
## epiweek_date_notification_2S37_2023 -2.627e-28 2.686e-15 0.000e+00 1.0000
## epiweek_date_notification_2S38_2023 -3.324e-28 2.645e-15 0.000e+00 1.0000
## epiweek_date_notification_2S39_2023 -2.543e-28 2.694e-15 0.000e+00 1.0000
## epiweek_date_notification_2S40_2023 -3.135e-28 2.858e-15 0.000e+00 1.0000
## epiweek_date_notification_2S41_2023 -2.752e-28 3.015e-15 0.000e+00 1.0000
## epiweek_date_notification_2S42_2023 -2.834e-28 3.174e-15 0.000e+00 1.0000
## epiweek_date_notification_2S43_2023 -2.944e-28 3.230e-15 0.000e+00 1.0000
## epiweek_date_notification_2S44_2023 -2.499e-28 3.371e-15 0.000e+00 1.0000
## epiweek_date_notification_2S45_2023 -3.188e-28 3.261e-15 0.000e+00 1.0000
## epiweek_date_notification_2S46_2023 -2.493e-28 5.612e-15 0.000e+00 1.0000
## epiweek_date_notification_2S47_2023 -3.196e-28 3.807e-15 0.000e+00 1.0000
## epiweek_date_notification_2S48_2023 -2.893e-28 3.464e-15 0.000e+00 1.0000
## epiweek_date_notification_2S49_2023 -2.916e-28 4.024e-15 0.000e+00 1.0000
## epiweek_date_notification_2S50_2023 -2.931e-28 4.330e-15 0.000e+00 1.0000
## epiweek_date_notification_2S51_2023 -2.911e-28 3.807e-15 0.000e+00 1.0000
## epiweek_date_notification_2S52_2023 -3.233e-28 4.795e-15 0.000e+00 1.0000
##
## (Intercept) ***
## epiweek_date_notification_2S02_2023
## epiweek_date_notification_2S03_2023
## epiweek_date_notification_2S04_2023
## epiweek_date_notification_2S05_2023
## epiweek_date_notification_2S06_2023
## epiweek_date_notification_2S07_2023
## epiweek_date_notification_2S08_2023
## epiweek_date_notification_2S09_2023
## epiweek_date_notification_2S10_2023
## epiweek_date_notification_2S11_2023
## epiweek_date_notification_2S12_2023
## epiweek_date_notification_2S13_2023
## epiweek_date_notification_2S14_2023
## epiweek_date_notification_2S15_2023
## epiweek_date_notification_2S16_2023
## epiweek_date_notification_2S17_2023
## epiweek_date_notification_2S18_2023
## epiweek_date_notification_2S19_2023
## epiweek_date_notification_2S20_2023
## epiweek_date_notification_2S21_2023
## epiweek_date_notification_2S22_2023
## epiweek_date_notification_2S23_2023
## epiweek_date_notification_2S24_2023
## epiweek_date_notification_2S25_2023
## epiweek_date_notification_2S26_2023
## epiweek_date_notification_2S27_2023
## epiweek_date_notification_2S28_2023
## epiweek_date_notification_2S29_2023
## epiweek_date_notification_2S30_2023 *
## epiweek_date_notification_2S31_2023
## epiweek_date_notification_2S32_2023
## epiweek_date_notification_2S33_2023
## epiweek_date_notification_2S34_2023
## epiweek_date_notification_2S35_2023
## epiweek_date_notification_2S36_2023
## epiweek_date_notification_2S37_2023
## epiweek_date_notification_2S38_2023
## epiweek_date_notification_2S39_2023
## epiweek_date_notification_2S40_2023
## epiweek_date_notification_2S41_2023
## epiweek_date_notification_2S42_2023
## epiweek_date_notification_2S43_2023
## epiweek_date_notification_2S44_2023
## epiweek_date_notification_2S45_2023
## epiweek_date_notification_2S46_2023
## epiweek_date_notification_2S47_2023
## epiweek_date_notification_2S48_2023
## epiweek_date_notification_2S49_2023
## epiweek_date_notification_2S50_2023
## epiweek_date_notification_2S51_2023
## epiweek_date_notification_2S52_2023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.01e-14 on 1430 degrees of freedom
## Multiple R-squared: 0.4999, Adjusted R-squared: 0.4821
## F-statistic: 28.03 on 51 and 1430 DF, p-value: < 2.2e-16#3 Sud-Ouest
donnee_sud_ouest <- subset(llcholeraclean, region_34 == "Sud-Ouest")
modele_sud_ouest <- lm(cas ~ epiweek_date_notification_2, data = donnee_sud_ouest)
summary(modele_sud_ouest)##
## Call:
## lm(formula = cas ~ epiweek_date_notification_2, data = donnee_sud_ouest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.95455 0.00000 0.00000 0.00000 0.04545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.000e+00 8.704e-02 11.489 <2e-16 ***
## epiweek_date_notification_2S33_2023 -4.184e-14 8.891e-02 0.000 1.00
## epiweek_date_notification_2S34_2023 -4.187e-14 8.989e-02 0.000 1.00
## epiweek_date_notification_2S35_2023 -4.182e-14 9.305e-02 0.000 1.00
## epiweek_date_notification_2S36_2023 -4.186e-14 8.839e-02 0.000 1.00
## epiweek_date_notification_2S37_2023 -4.188e-14 9.731e-02 0.000 1.00
## epiweek_date_notification_2S38_2023 -4.185e-14 9.305e-02 0.000 1.00
## epiweek_date_notification_2S39_2023 -4.190e-14 1.231e-01 0.000 1.00
## epiweek_date_notification_2S40_2023 -4.189e-14 1.066e-01 0.000 1.00
## epiweek_date_notification_2S41_2023 -4.188e-14 9.059e-02 0.000 1.00
## epiweek_date_notification_2S42_2023 -4.545e-02 8.900e-02 -0.511 0.61
## epiweek_date_notification_2S43_2023 -4.186e-14 9.535e-02 0.000 1.00
## epiweek_date_notification_2S44_2023 -4.186e-14 9.401e-02 0.000 1.00
## epiweek_date_notification_2S45_2023 -4.185e-14 1.231e-01 0.000 1.00
## epiweek_date_notification_2S46_2023 -4.185e-14 1.005e-01 0.000 1.00
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08704 on 126 degrees of freedom
## Multiple R-squared: 0.03864, Adjusted R-squared: -0.06818
## F-statistic: 0.3617 on 14 and 126 DF, p-value: 0.9829